Enterprise AI Analysis
CDRPE: A Combined Deep Learning and Self-Attention Enhanced Reinforcement Learning Framework for Automated Compact Model Parameter Extraction
This research introduces CDRPE, a novel framework that integrates deep learning (DL) and self-attention enhanced reinforcement learning (RL) for automated compact model (DCM) parameter extraction. It addresses the challenges of manual extraction in modern semiconductor technology by offering an efficient, robust, and physically consistent solution. The framework excels in handling high-dimensional parameter spaces, achieving faster convergence and superior accuracy compared to existing black-box optimization and RL methods, with demonstrated success in BSIM4, BSIMCMG, and BSIMSOI models.
Executive Impact
CDRPE streamlines semiconductor design, delivering critical performance and efficiency gains for enterprise-level circuit development.
0
Parameters Extracted
0
RMSE Below
0
Speed Up (vs. other RL)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Target Electrical Characteristics
→
DL-based Core DCM Parameters Initialization
→
RL-based Full DCM Parameters Fine-tuning
→
Extracted Compact Model
15-20
Core DCM Parameters Initialized by MLP
| Feature | SAE-RL | Traditional RL | Black-Box Opt. |
|---|---|---|---|
| Action Space | Continuous | Discrete | Continuous/Discrete |
| Scalability (Parameters) | High (100+) | Low (<20) | Medium (<50) |
| Convergence Speed | Fastest | Medium | Slow |
| Physics Guidance | Integrated | Limited | None |
| Generalization | High | Medium | Low |
BSIM4 Model Parameter Extraction
CDRPE successfully extracted 54 parameters for BSIM4 with an RMSE of 2.94%, significantly outperforming PPO (3.36%) and DDPG (4.09%). This represents a 2.7–5.1X speed-up over other RL methods and 6.4–7.7X over black-box optimization techniques. The extracted models showed excellent agreement with TCAD and silicon data, maintaining physical consistency across various bias conditions and device geometries.
BSIMCMG and BSIMSOI Model Extraction
The framework demonstrated its robustness by extracting up to 100 parameters for advanced FinFET BSIMCMG and 70 parameters for SOI BSIMSOI models. Achieved RMSE values were below 5% for I-V and C-V characteristics. Circuit-level simulations confirmed the practical applicability, with generated models showing good convergence in both digital and analog circuits.
Calculate Your Potential ROI
Estimate the impact CDRPE could have on your semiconductor design and manufacturing processes.
Estimated Annual Savings
$0
Annual Engineering Hours Reclaimed
0
Your Implementation Roadmap
A phased approach to integrating CDRPE into your workflow, ensuring a smooth transition and maximum impact.
Phase 01: Initial Assessment & Pilot
Evaluate current parameter extraction workflows and identify key integration points. Conduct a pilot program with CDRPE on a selected set of DCMs to demonstrate initial efficacy and gather feedback.
Phase 02: Full Integration & Training
Integrate CDRPE into your existing EDA environment. Provide comprehensive training for your engineering teams on leveraging the DL/RL framework for various compact models.
Phase 03: Optimization & Scaling
Fine-tune CDRPE settings for optimal performance across all device types and technology nodes. Scale the framework to handle large-scale parameter extraction tasks, ensuring high accuracy and efficiency.
Phase 04: Continuous Improvement & Support
Establish a feedback loop for continuous improvement and algorithm updates. Access ongoing technical support and consultations to ensure long-term success and adaptation to evolving needs.
Ready to Transform Your Extraction Process?
Schedule a personalized session with our AI specialists to discuss how CDRPE can be tailored for your specific needs.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat